Method and system for optimum placement of advertisements on a webpage

ABSTRACT

A method and system for placement of graphical objects on a page to optimize the occurrence of an event associated with such objects. The graphical objects might include, for instance, advertisements on a webpage, and the event would include a user clicking on that ad. The page includes positions for receipt of the object material. Data regarding the past performance of the objects is stored and updated as new data is received. A user requests a page from a server associated with system. The server uses the performance data to derive a prioritized arrangement of the objects on the page. The server performs a calculation regarding the likelihood that an event will occur for a given object, as displayed to a particular user. The objects are arranged according to this calculation and returned to the user on the requested page. The likelihood can also be multiplied by a weighting factor and the objects arranged according to this product.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of priority by virtue of its filing as adivision of U.S. application Ser. No. 09/285,929, now U.S. Pat. No.6,907,566, filed Apr. 2, 1999.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and system for optimizing theplacement of graphical objects., e.g. advertisements (ads), topic tiles,or the like on a page, e.g. a webpage, so that an event associated withthe objects is more likely to occur. Such an event would include theincidence of a user identifying, or clicking on the object with apointing device.

2. Description of Related Art

The Internet provides a fast, inexpensive, and convenient medium forinformation providers to make information available to users on awebsite. Information in such websites might include, for example, sportsscores, movie reviews, daily news, stock quotations, and the like. Whilepassword protected pay-sites exist on the internet, websites cangenerally be accessed at no cost to the user. This presents a problemregarding revenues returned in relation to providing a website full ofinformation. Some website providers are funded to distribute variousinformation to the public,. for example NASA (National Aeronautics andSpace Agency) or other such public agencies. Still other providersutilize their website as a commercial means in itself to sell variousproducts, such as books or compact discs. Regardless of such externalfunding, the generation of revenue from a website is proving to beincreasingly important, as there are costs inherent in creating,providing, and maintaining a website. Moreover, as Internet trafficcontinues to increase, the opportunities for the generation of revenuein association with those contacted sites also tends to increase.

In response to such concerns, website providers are increasing theamount of advertising space on their webpages in order to generate morerevenues. The advertisements (or ads) appear as banners, blocks, ortiles on various portions on the webpage. Typically, an advertisementserves as a click-through point to sources of more information aboutthat particular advertiser or topic. In other words, the advertisementexists as a graphical object which has a link to other information. Theuser typically chooses or identifies the object by clicking on it with acomputer pointing device. The identification of the object invokes thelink, and hence is often referred to as “click-through.”

As with newspapers and other such advertising mediums, factors such asthe location and size of the ad on a webpage will affect the pricecharged. Ads appearing at the start of the webpage will usually commanda higher price than ads appearing at the end. This is particularly truefor ads which appear further down from the initial webpage screen (aslimited by the size of the user's display device). Web browsers usuallyrequire the positive act of a user scrolling down a page in order toview an ad located further down from the top. A user who sifts throughweb pages based upon the initial material visible on the page oftenoverlooks such lower placed ads. Ultimately, each advertiser wants totheir ad to be seen, and to increase the amount of click-throughs, orviewing traffic, which visits its particular website or webpages as aresult of a click-through on its ad.

Generally, most sites sell their advertising as a function of cost perthousand impressions, where an impression is counted as an instance ofthe ad appearing on a webpage. Ads can be randomly placed on a webpage,or advertisers might choose locations on the page. In the latterinstance, advertisers might be required to spend considerable time,money, and resources deciding where to place their advertisements, withthe hope and anticipation of their ad being noticed, read, and evenacted upon by the user. For instance, ads directed to younger Internetusers might be placed on websites related to young celebrities, popculture, or modern music. The ad might need to be placed near the top ofthe webpage in order to attract attention to the ad. This might requirea costly expenditure by the advertiser, and would carry with it noassurances that the ad will attract any significant click-throughtraffic. As a result, the advertiser might be dissuaded altogether fromplacing the ad on a particular website or webpage. For every suchdecision by an advertiser not to place an ad, the revenues for a websiteor webpage which depends upon such revenues will be adversely affected.

Yet another way of selling advertising on the Internet is by chargingthe advertiser a certain amount for each click-through that occurs on aparticular ad (often referred to as cost-per-click, or CPC). Suchpricing structures might ultimately attract more attention fromadvertisers because the advertiser will not be required to pay unlessthe ad actually attracts click-through traffic. However, this pricingscheme shifts the impetus for deciding optimum ad placement back to thewebsite or webpage provider, as no revenue will be generated for theprovider if the user never clicks upon an ad.

Accordingly, a method and system are needed in this field which willserve to increase the chance of an event occurring for an object whichis presented on a page. In the Internet context, a method and system areneeded which would increase the amount of click-through traffic on adspresented on a webpage, and thereby increase the revenue generated by awebsite provider which sells ads on that webpage.

SUMMARY OF THE INVENTION

The present invention provides a method and system for placement ofgraphical objects on a page to optimize the occurrence of an eventassociated with such objects. The graphical objects might include, forinstance, advertisements on a webpage, and the event would include auser clicking on that ad. The page includes positions for receipt of theobject material. Data regarding the past performance of the objects isstored and updated as new data is received. A user requests a page froma server associated with system. The server uses the performance data toderive a prioritized arrangement of the objects on the page. The serverperforms a calculation regarding the likelihood that an event will occurfor a given object, as displayed to a particular user. The objects arearranged according to this calculation and returned to the user on therequested page. The likelihood can also be multiplied by a weightingfactor and the objects arranged according to this product.

As applied in context to an Internet based system, the present inventionutilizes a unique system of gathering and grouping information abouteach particular user to the system, and then uses this information tooptimize the event, or click-through traffic, for a particular graphicalobject, e.g. an ad, or set of ads, presented to that user. Optimizationis achieved by calculating a click-through-percentage for a particularad based upon sorted and categorized information about a particularuser. This click-through percentage will consist, in part, of anestimation of the likelihood that a particular user will actually clickon the ad presented. The click-through percentage is then used to groupthe ads, usually in descending order of calculated percentage, in theappropriate spots on a webpage. The ads might also be grouped accordingto click-through percentage times the cost-per-click for each ad. Topictiles might also be displayed according to a similar formula, includingfor instance click-through-rate times the revenue-per-user.

According to either formula for ad placement, the revenue for thewebsite provider will be significantly increased, as each click-throughby a user will be more likely to occur, and also the page will bestructured to generate an increased amount of revenue for eachclick-through. It has been found that the random placement of ads on awebpage yields a click-through-percentage of approximately 2-3%. Byarranging the ads on a page in descending order ofclick-through-percentages (e.g. higher to lower), the generalclick-through rate has been found to at least double. By arranging theads in descending order of click-through percentages timesprice-per-click, the overall revenue rate has been found to at leasttriple.

Other features can be summarized as follows: as a user interacts withvarious Internet sites, a file of information called a “cookie” isgenerated and maintained on a user's hard disk. A cookie typicallyrecords a user's preferences when using a particular site. A cookie is amechanism that allows a server to store a file about a user on theuser's own computer. The present invention includes providing a websitewhich gathers and utilizes such information from the cookie file, butalso generates and maintains a centralized database of informationconcerning each user. If a user is new to the site, then the user isredirected to areas where information about that user can be gathered.As the user proceeds through various website areas relating to topicssuch as movies or horoscopes, information such as age or zip codes canbe gathered and stored for each particular user under a useridentification (ID) number or tag. The data from the users is thenanalyzed, delineated, and placed in different groupings, or “bins.” Adevice is used which creates meaningful bins, or in other words, bins ofpersons which have discernable behavioral differences between them.These bins might include, for example, demographical data such aspersons of a certain income level, gender, or age grouping. This timeintensive task of analyzing user information and creating different binsis performed as a background task, and therefore does not adverselyaffect the overall speed of the system.

An ad server device is also used which queries the system forinformation about each particular user. The bins of information are usedto calculate a click-through-percentage for each of the various adsavailable, based upon an analytical method which includes, among otherthings, parameters relating to the user's information, the categorizedbins of data, and the prior performance information for a particular ad.This system will allow multiple bins to be used for a performancecalculation without adversely affecting the speed of the calculation. Ifan ad is new to the system, a performance estimation is made which willallow convergence toward the true performance percentages throughsubsequent click-throughs and related calculations for that ad. A set ofads is then returned by the ad server for display to a particular useron the contacted website and associated webpages. The performancecalculation for each ad, along with its price-per-click, are used todetermine placement of the ads on a website for optimum click-throughoccurrences and generation of revenue.

The system might also include an ad performance interface which willallow an advertising client to access various ad performance informationfrom an ad performance database relating to the click-through-percentageand success of each ad. Yet another interface might be provided whichwill allow an advertiser to place ads directly into an ad database foraccess by the ad server.

Therefore, according to one aspect of the present invention, a system isprovided wherein a page is requested by a user, with the page haspositions for placement of graphical objects. Each object has associatedwith it a link to other information, and a certain event will invokethat link. Certain performance data is stored regarding the occurrenceof events for objects in the system. The performance data is used tocalculate a likelihood for each object that the event will occur forthat particular user. The page is then returned to the user with a setof graphical objects arranged on the page, the objects positionedaccording to their event likelihood calculation.

According to another aspect of the present invention, a more specificexample is provided. Namely, a system is provided wherein a website andassociated webpages are made available by a web server, with the pageshaving ads arranged to provide optimized click-through generation ofrevenue deriving from the ads. The system gathers information relatingto a user and stores this information in a central database under a useridentification tag which is passed back to the user as part of thecookie data. The user data is further grouped into a variety of binsaccording to behavioral differences associated with such groups. Aclick-through-percentage is calculated for each ad based upon the userinformation, the associated bins, and the prior click-through-percentageassociated with the ad. The ads are arranged on the webpage indescending order according to the calculated click-through-percentagefor each ad.

According to another aspect of the present invention described above,the ads are arranged on the webpage in descending order according to thecalculated click-through-percentage for each ad times theclick-through-price for each ad.

Another aspect of the present invention described above displays topictiles in descending order according to click-through-rate for aparticular tile, times the revenue-per-user.

In still another aspect of the present invention described above, thedevice which groups the user data into a variety of bins is configuredto perform its task periodically in the background, thereby minimizingslow down of the overall system.

Yet another aspect of the present invention provides an ad performancedatabase, and an interface for the advertising client to access theperformance database and review the performance parameters relating to aparticular ad displayed according to this method.

A further aspect of the present invention provides an ad content andplacement database, and an interface for the advertising client to placeads directly into the system.

Other aspects and advantages of the present invention can be seen uponreview of the figures, the detailed description, and the claims whichfollow.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is an example of a prior art webpage in which ad sites arearranged to randomly receive placement of ads.

FIG. 2 is an example of a webpage according to the present invention inwhich the ads (or topic tiles) are arranged in descending order by theirclick-through-percentage (CTP) or CTP times price-per-click (CPC).

FIG. 3( a) is a block diagram of the functional components used forarranging the ads according to FIG. 2.

FIG. 3( b) is a block diagram of the Relational Ad (RAD) Servercomponent of FIG. 2.

FIG. 4 is a block diagram of a sequence of interactions between theuser, web site, and Recognizer elements of FIG. 3( a), wherein a user isnew to the optimizer network.

FIG. 5 is a block diagram of a sequence of interactions between theuser, web site, and Recognizer elements of FIG. 3( a), wherein a user isnew to the web site, but is already recognized by the optimizer network.

FIG. 6 is block diagram of a sequence of interactions between the user,web site, and Recognizer elements of FIG. 3( a), wherein a user hasalready visited the web site, and is already recognized by the optimizernetwork.

DETAILED DESCRIPTION

The present invention provides a method and system for optimizing theevent occurrences for graphical objects on a page. More specifically andfor discussion purposes, a method and system are provided for optimizingrevenues generated by a webpage. Optimization occurs through ranking ofthe ads (or topics) according to a click-through-percentage generatedfor each ad. The page might further be optimized by ranking the adsaccording to cost-per-click multiplied times theclick-through-percentage. This will create a webpage that has both ahigh likelihood of click-throughs, and maximum revenue for eachclick-through that actually occurs. A detailed description of theinvention is provided with respect to FIGS. 1-6.

FIG. 1 shows a generalized block diagram layout of a prior art webpage10. This page contains a web page title block 12, and a web page contentblock 14. A sequence of ad sites 16-20 are shown which receive anddisplay ads configured to fit in these spots. In the past, such ads havebeen placed on the page according to an advertisers preferred (and/orpaid for) page location. Alternatively the ads have been randomly placedupon the page, with some consideration for not repeating ads which auser might have seen before. The randomized placement of ads on awebpage has been found to result in a click-through-percentage for eachad of approximately 2-3%.

FIG. 2 shows an example block diagram layout of a webpage 40 accordingto the present invention. A generalized web page content block 42 isshown in the right-center of page 40. In addition, the peripheral blocksfor placement of ads, or topic tiles, are arranged in order to maximizerevenue generation for the webpage. Note that a topic tile might consistof a click-through point for more sites and information about aparticular topic, including for instance horoscopes or personals ads. Inone embodiment, the most prominent block on the page, e.g. the uppermostbanner block 44, will carry ads that have the highest probableclick-through-percentage for a particular user. Ads with the nexthighest calculated click-through-percentage will be displayed in thenext most prominent spot on the page, and so forth. The ads aretypically grouped from top to bottom in descending order of calculatedclick-through-percentage, as shown by blocks 46-50. If an ad spot isdesignated as more prominent, i.e. the ad spot is located in theunscrolled center of the user's display, then theclick-through-percentage ranking and display of ads will follow therelative prominence designations for a particular page. Webpagesarranged according to this method have been found to generate at leasttwice the revenue of webpages having randomly place ads.

In yet another embodiment, the ads can also be sorted and displayedaccording to a method which multiplies the calculatedclick-through-percentage times the cost-per-click for each ad. Underthis method, the cost that the advertiser pays for each individualclick-through on an ad will factor into the placement of the ad on thewebpage. A more expensive ad with a moderate click-through-percentagemight earn a more prominent position than an ad with a highclick-through-percentage, but having a low price-per-click. If themultiplied result of the two factors produces a higher number, than therevenue generated from display of that particular ad will also behigher. Hence, such an ad will be displayed in more prominent positionon the webpage in order to encourage more click-throughs by the user.Webpages arranged according to this method have been found to generateat least three times the revenue per page over webpages having randomlyplaced ads.

FIG. 3( a) shows a block diagram of a system or network 100 foroptimizing placement of ads on a webpage according the arrangementmethods described above. While the elements are discussed in a certainorder below, many of the processes occur simultaneously, or in otherorder sequences as necessary. As shown, a user 102 contacts a website104 and requests a page 106. During the process of interacting with theweb site, the user 102 will provide personal information 108 such astheir birthday, gender, zip code, and the like. This information is sentfrom the web site 104 to a component used for recognizing certaincharacteristics about a user, hence referred to as the Recognizer 110.Depending upon the status of the user (e.g. new to the network, new tothe website, or known by the system), the interactions of the user,website, and Recognizer will vary. A centralized database is used,however, to store various information which has been collected about auser. The information is accessible via a user identification (Id) tagor number, which is created for each user. The interactions of the user,website, and Recognizer are detailed further in the discussion of FIGS.4, 5, and 6 below.

In essence, when a site wants to show a page, it contacts an ad servercomponent 112, shown at the center of the network 100 in FIG. 3( a).This device might also be referred to as a relational server component,and is hence referred to as the Rad Server. The site contacts the RadServer and indicates which webpage and website will be shown, as well asthe centralized Id of the user who will view the page. The Rad Server112 performs the overall function of gathering the necessary informationregarding a particular ad (or set of ads) and the particular user andgenerating a set of ads which have been optimized for placement on awebpage according to a calculated click-through-percentage (orclick-through-percentage times price-per-click) for that user.

Referring now to both FIGS. 3( a) and 3(b), additional operational stepsof the Rad Server are shown. Initially, the Rad Server 112 will querythe Recognizer 10 for as much information 114 as is known about theparticular user. The Recognizer then returns the information requested116 back to the Rad Server 110 for use in requesting possible ads forplacement and performing ranking calculations regarding those ads.

In order for the optimizer system to have ready access to a large storeof ads, an ad/content placement database 118 is provided for storing aplurality of ads, which might be used for possible display. Thead/content placement database 118 might contain, for example,information about each ad contract, e.g. price per impression,price-per-click-through, constraints on pages or positions where the admay be placed, and/or constraints on demographic variables which musthold for the ad to be presented. The database 118 might also containinformation associated with different page layouts, e.g. the number ofbanner or ad spots available.

Such ads are created and/or purchased by the advertiser 120 who mightuse an ad placement interface 122 (which is optional) to place ads 124in the database 118. The interface 122 could be web accessible and wouldguide the advertiser 120 through the necessary steps for creating anduploading an ad into the database 118. Alternatively, the generalcontent of the ads is created and/or licensed 126 by administrators ofsuch accounts and entered 128 into the ad/content placement database118. The Rad Server 112 requests possible ads or content material 130based upon information from the particular user 102 from the ad/contentplacement database 118. The database 118 then returns the possible ads132 for placement on the webpage that fit the particular characteristicsof the user 102.

With the possible ads 132 now collected, as shown by element 131 in FIG.3( b), the Rad Server 112 performs a click-through percentagecalculation for each ad, as shown by element 133 in FIG. 3( b). Thiscalculation further requires access to performance information for eachad. Accordingly, an ad/content performance database 140 is providedwhich stores click-through-percentage data for each ad, as well as dataconcerning the grouping of users into different categories, or bins. Aprocessing device, hereafter referred to as the Arbitrator 150, takesinformation gathered and stored about the users and processes thisinformation into useful bins. The user data is sampled and bins arecontinually created which differentiate users in optimal ways forplacement of ads. For example, a bin of all users under 14 years oldmight be created as one separate bin, rather than all users under 18years old. This strategy for categorizing users becomes important whentrying to predict or calculate a click-through-percentage for a givenad, or set of ads.

Referring again to FIG. 3( a), when a user clicks on a particular ad asshown by 107, a click-through tracker 109 is provided to track and thenrecord the click-throughs 111 into a log file 113. The log file 113 alsocollects ad impression data 121 from the Rad Server 112. The log file113 outputs the log data 115 into a device referred to as a log digester152. The log digester 152 interacts with the Arbitrator 150, as shown bydataflows 117 and 119. The log digester 152 processes through all theuser data, and places each bit of user data in its appropriate binaccording to directions from the Arbitrator 150. The Arbitrator 150 runsperiodically to determine how best to differentiate among users. Themore differences that the Arbitrator finds, then the more efficiently itwill be able to deliver ads that users will click on, or content thatusers will enjoy. Both the Arbitrator 150 and log digester 152 functionsare generally time intensive operations and can take significantprocessing resources. In this embodiment, these tasks are shown to runin the background so as not to slow down the overall system performance.Typically, the Arbitrator 150 will be configured to interact with thelog digester 152 every 15 minutes or less and the log digester 152 willoutput processed data to the ad/content performance database 118. Thead/content performance database 118 is therefore a static database thatis updated periodically from the log digester 152. The update rate isvariable and can be further improved through via system codeoptimization, increased processor speeds, dedicated hardware, and thelike.

Referring again to FIGS. 3( a) and 3(b), the Rad Server 112 sends arequest 134 for performance statistical data (or performance stats) tothe Ad/Content performance database 140 and the requested performancestats 136 are returned to the Rad Server 112. A click-through-percentage133 is calculated for each ad based upon the performance stats and theuser information. The Rad Server 112 thereafter ranks the ads accordingto a desired arrangement method 135. While other equivalent methods areintended to be included within the scope of this invention, the methodsdiscussed above include arranging the ads according to:click-through-percentage; or click-through-percentage timesprice-per-click for each ad. Topical tiles might also be arrangedaccording to the click-through-rate for each topic, times therevenue-per-user.

Referring again to FIG. 3( a), the website 104 requests ads from the RadServer 112 as shown by dataflow 160. After the steps described above areperformed, the Rad Server 112 delivers a set of ads for display to theuser which have been optimized for increased click-throughs, and/orincreased revenue generation for the webpage provider.

Yet another interface 170 might (optionally) be provided which wouldprovide the Advertiser 120 with the ability to monitor and track theperformance of their ads. The ad performance interface 170 would collectperformance stat data 172 from the ad/content performance database 140.The interface would thereafter provide user-friendly and viewable data174 to the client regarding detailed stats, including for instancedemographic profiles of who is clicking on their ads. Such informationcould prove invaluable to the advertiser for targeting future customerswith particular ads. The information would also serve to demonstrate thesuccess rate, and thereafter aid in setting the pricing structure ofads, in order for the network provider to further increase revenues.

The optimizer 100 further uses a unique sequence of steps to gatherinformation from each particular user. These sequences are shown inFIGS. 4, 5, and 6. Normally, a cookie is used by websites to detectinformation about a user. A cookie is a special text file that a websitestores on the user's harddrive. Typically a cookie records a user'spreferences when using a particular site. Using the Internet's HypertextTransfer Protocol (HTTP), each request for a webpage is independent ofall other requests. For this reason, the webserver generally has nomemory of what pages it has sent to a user, or information about thatuser. A cookie is a mechanism that allows the server to store its ownfile about a user on the user's own computer. The file is typicallystored in the subdirectory of the browser directory. The cookiesubdirectory will contain a cookie file for each website which a userhas visited, and which uses cookies. Cookies have been previously usedto rotate the ads that a site sends so that a page does not keep sendingthe same ad as it sends a succession of requested pages. Cookies havealso been used to customize pages based upon the browser type.Generally, users must agree to let cookies be saved for them, and suchis the common practice as it speeds up web service. Yet another practiceis for a user to create a file of personal information, or a profile,for use by a contacted website.

According to the present invention, the previously mentioned centralizedId number or tag is created for each user and provides access to storedinformation about the user within the optimizer system. When a sitelearns a new piece of information about a user, e.g. zip code, thisinformation is sent to the Recognizer which enters this information intothe centralized database. While many different forms of databasing wouldprovide an equivalent result, the preferred embodiment uses anon-relational database that has been written for scalability and speed.When a site queries the Rad Server for a set of ads to place on a page,the site passes the centralized Id to the Rad Server, which in turnrequests any relevant information associated with that user Id from theRecognizer database. The Recognizer database might also be queried byindividual site, e.g. for dynamically targeted content generation.Separate authentication would be provided for read and write access tothe Recognizer database.

Hence, when the Rad Server 112 requests user information, then thedatabased information can be readily provided via the Id. FIG. 4 showsthe sequence of steps that occur when a user is new to the network. Instep (a) the user (U) 200 sends a request 202 to the web server (W) 204for a page of information. In step (b), the web server 204 redirects 208the user 200 to the Recognizer (R) 206. In step (c), the user request210 is redirected via a redefined URL (uniform resource locator). TheRecognizer 206 assigns a new Id to the user and saves it in a database.In step (d), the Recognizer 206 redirects 212 the user 200 back to theweb server 204 with the user Id appended to the URL. The Recognizer 206also sends a Recognizer cookie file 214 back to the user 200. In step(e), the user 200 sends a request 216 for the original page desired, butwith the Id appended. In step (f), the web server 204 returns its owncookie 218 with the Id, along with the webpage 220, which the userrequested (with the ads optimally arranged).

FIG. 5 shows the sequence of steps which occurs when the user is new tothe web server, but has already been databased in the optimizer network.In step (a), the user 200 requests 222 a webpage from the web server. Inthis instance, the website has not been previously visited by the user.In step (b), the web server 204 redirects the user 200 to the Recognizer206. In step (c), the user 200 requests 228 the URL from the Recognizer206. Since the user has already visited the optimizer network, they havea cookie 226 which is passed back to the Recognizer 206. In step (d),the Recognizer 206 redirects 230 the user 200 to the site with the Idappended. In step (e), the user 200 sends a request 2232 for theoriginal page desired, but with the Id appended. In step (f), the webserver 204 returns its own cookie 234 with the Id, along with thewebpage 236, which the user requested (with the ads optimally arranged).

FIG. 6 shows a sequence of steps which occurs when the user has alreadyvisited an optimizer network site. In step (a), the network has alreadyestablished a cookie for the user with the centralized Id. TheRecognizer 206 is therefore not involved in the interaction. The user200 sends a request 240 for a webpage to the web server 204, along withthe existing cookie file 238. In step (b), the web server 204 respondsby sending the requested webpage 242 (with the ads optimally arranged).

In each case, the website will request HTML code from the Rad Server 112to place in the appropriate advertising blocks of the webpage. TheServer outputs this information to the user, and the information isthereafter decoded and arranged by the user's web browser. When the userclicks on an ad, they are redirected through optimizer so that theclick-through can be counted, and the user is thereafter sent to the URLspecified by the Advertiser.

In providing further details to elements described above, the Arbitrator150 in FIG. 3( a) has the task of creating many different bins of dataas characteristics about the users are learned and delineated. Normally,when any new factor (e.g. a new bin) is introduced into a system likethe present, the complexity of implementation increases greatly, as eachfactor will typically need to be multiplied by every other existingfactor in the system. As a result, variables must be partitioned into asmall number of equivalence classes in order to make feasible thelearning problem. This creates pressure towards choosing a small numberof bins for each variable. However, in the limit that there is onlypartition (one type of user), the learning problem is greatlysimplified, but the available information is not being maximallyexploited for monetary gain. This is the classic“Information/complexity” tradeoff in learning theory: the more powerfulthe model, the more difficult it is to learn the parameters.

While a variety of solutions might be applied, the preferred embodimentof the present invention applies a classical statistical technique forhypothesis testing, i.e. the generalized likelihood ratio test, asfollows. Starting with a given a particular random variable X whichtakes values in the set S_(X), and a set of ads A such that for each ada∈A and value x∈S_(X), the result includes associated impressions countsI_(X)(x,a) and click-through counts C_(X)(x,a). Next, consider afunction ƒ: S_(X)→S_(B) which assigns values in S_(X) to equivalenceclass labels in S_(B). Associated with each b∈S_(B) are the impressionand click-through counts I_(B)(b,a)=Σ_(x|ƒ(x)=b)I_(X)(c,a) andC_(B)(b,a)=Σ_(x|ƒ(x)=b)C(x,a), respectively. These counts can be used toassign a score to ƒ for a given ad a∈A via

${\phi\left( {f,a} \right)} = {{2\left( {\sum\limits_{b \in S_{B}}\;{\max\limits_{\lambda}\left\{ {\log\;{p\left( {{I_{B}\left( {b,a} \right)},{C_{B}\left( {b,a} \right)},\lambda} \right)}} \right\}}} \right)} - {2{\max\limits_{\lambda}\left\{ {\sum\limits_{b \in S_{B}}{\log\;{p\left( {{I_{B}\left( {b,a} \right)},{C_{B}\left( {b,a} \right)},\lambda} \right)}}} \right\}}}}$Where p is standard binomial likelihood given by

${p\left( {c,n,\lambda} \right)} = {\begin{pmatrix}n \\c\end{pmatrix}{\lambda^{c}\left( {1 - \lambda} \right)}^{n - c}}$

This test can be interpreted as measuring the difference between the“explanatory power” that is achieved by assuming that the click-throughrate for the ad in question varies in any fashion across the equivalenceclasses in question (first term), versus the “explanatory power”obtained by assuming that the click-through rate is identical acrossequivalence classes (second term).

The asymptotic distribution of ø is known to be X² with degrees offreedom |S_(B)|−1, which allows proper normalization of the score withrespect to number of equivalence classes. The following formula is usedto transform ø into a random variable approximately distributed aszero-mean unit-variance Gaussian.

${z\left( {f,a} \right)} = {\left( {\sigma\left( {{S_{B}} - 1} \right)} \right)^{- 1}\left( {\frac{\phi\left( {f,a} \right)}{{S_{B}} - 1} - {\mu\left( {{S_{B}} - 1} \right)}} \right)^{1/3}}$${\mu(n)} = {1 - \frac{2}{9n}}$ ${\sigma(n)} = \sqrt{\frac{2}{9n}}$As can be seen from the formula, this normalization discouragespartitioning into a large number of equivalence classes (i.e., large|S_(B)|); however, if the increase in explanatory power (i.e., ø) issufficiently large it can overcome this “bias” against fine-grainedpartitioning. In this manner the information/complexity tradeoff isrepresented.

The above score is averaged over the current population of ads to assigna score Q to the potential partitioning ƒ, Q(ƒ)=Σ_(a∈A)z(ƒ,a). Inprinciple, a procedure to enumerate all possible partitionings andchoose the one with the largest score Q is possible, but (currently) inpractice a human suggests several possible ways to partition the data, Qis calculated for each candidate partition, and the partitioning withthe highest score is used in the online system. Since the complexity ofmodel supported by the data increases with the amount of data, theappropriate partitioning for the system can change with time, e.g., ifthe amount of visitors to the site increases dramatically. Thus thearbitrator is used both when first introducing a variable into thesystem, and for periodically tuning the distinctions the system makes inorder to maximize performance.

In providing further detail to certain elements described above, element133 in FIG. 3( b) describes a click-through-percentage calculation whichis performed for each ad. This calculation provides a probability orlikelihood, expressed as a percentage, that a user will click on aparticular object or ad. While many different processes might be usedwithin the scope of optimizing revenue generation through the placementof ads by using click-through-percentage, the present invention employsthe technique further detailed as follows: The value of placing an adincludes a fixed, known amount of revenue per impression (possibly evenzero), plus some amount of revenue that would be generated if the adwere clicked on. Since clicking on the an ad is a random event, the Radserver attempts to estimate the average amount of revenue that resultsfrom click-throughs, which is given by the probability that the userwill click on the ad times the amount of revenue generated when the adis clicked on. The ad server is therefore attempting to maximize, onaverage, the revenue resulting from a particular assignment of ads tothe page. The ad server estimates the probability of clicking on an adusing formulas derived from Bayesian statistical methodology, whichclick-through modeled as a binomial process, and with a prior model ofadvertisement appeal given by exponential distribution parameterized bya single parameter p₀.

First, the system should determine the value of factors used inpredicting performance. Demographic information associated with a userId is retrieved from the Recognizer. Other information is obtained fromthe site requesting the ad placement, e.g the page the ads will be shownon. Still other information, e.g. the time of day, is determined by thead server.

Second, for each possible assignment of an ad to a spot on the page, andfor each factor whose value is known, the ad server obtains the numberof impressions and click-throughs seen for that ad in that spot with thefactor in question. These counts are used to estimate the likelihood pthat the user will click on the ad, according to the following formula:

$\begin{matrix}{{\hat{p}\left( {q,s,p_{0}} \right)} = \frac{\phi\left( {q,s,p_{0}} \right)}{{\phi\left( {q,s,p_{0}} \right)} + {\varphi\left( {q,s,p_{0}} \right)}}} \\{{\phi\left( {q,s,p_{0}} \right)} = {{\alpha\left( {{c(s)},{i(s)},p_{0}} \right)}{\prod\limits_{i = 1}^{N}\;{\theta\left( \left. {\left. {\left. {\left. {c\left( {s{q_{i}}} \right.} \right),{{{c\left( s \right.}}q_{i}}} \right) + {i\left( {s{q_{i}}} \right.}} \right),{\theta\left( {{{c\left( s \right.}}q_{i}} \right)},{c(s)},{r(i)}} \right) \right.}}}} \\{{\varphi\left( {q,s,p_{0}} \right)} = {\left( {1 - {\alpha\left( {{c(s)},{i(s)},p_{0}} \right)}} \right){\prod\limits_{i = 1}^{N}{\theta\left( \left. {\left. {\left. {\left. {n\left( {s{q_{i}}} \right.} \right),{{{c\left( s \right.}}q_{i}}} \right) + {i\left( {s{q_{i}}} \right.}} \right),{\theta\left( {{{n\left( s \right.}}q_{i}} \right)},{n(s)},{r(i)}} \right) \right.}}}} \\{{\theta\left( {a,b,k} \right)} = \frac{a + 1}{b + \left( {1/k} \right)}}\end{matrix}$α(a,b,p ₀)=η(b,p ₀)−√{square root over (η²(b,p ₀)−8(2+a)p ₀)}{squareroot over (η²(b,p ₀)−8(2+a)p ₀)}η(b,p ₀)=2+(2+b)p ₀n(s)=i(s)−c(s)n(s|q _(i))=i(s|q _(i))−c(s|q _(i))where

-   q=context vector-   c(s)=clicks count for content s-   i(s)=impression count for content s-   c(s|q_(i))=click count for content s given factor i takes value    q_(i)-   i(s|q_(i))=impression count for content s given factor i takes value    q_(i)-   r(i)=total possible values for factor i

These equations incorporate the assumptions that factors areconditionally independent, factor values are distributed multinominallywith a product exponential prior peaked at a uniform distribution, andthat clickthroughs not conditioned on factor values are distributedbinomally with an exponential prior peaked at p₀. p₀ is purposely chosento overestimate the probability of click-through so that the estimatorconverges to the actual probability of click-through from above. This isparticularly true for new ads in which little (or no) information isknown. A less efficient system might, for instance, randomize all theads 20% of the time, then measure the relative performance, and thenoptimize the placement of ads. The present system, however, proves to bemuch more efficient as optimization is constantly being performed. Thepresent system serves to automatically balance the opposing goals ofgathering data on newer ads and exploiting information about older ads.In this way, the number of impressions is greatly reduced which thesystem might need to show in order to make an accurate prediction ofclick-through rate for a particular ad.

Once derived, the list of possible assignments of ads to particular adspots is sorted in descending order of expected revenue. While there aresubsequent empty spots, the ad server examines the next assignment onthe list, and accepts the assignment unless it would violate a placementconstraint, in which case it is discarded. If possible assignments areexhausted before the page is filled, the Rad Server might then fill theremaining positions with a canonical identifier indicating the spot isto remain empty. The list of acceptable assignments in then returned tothe requesting website.

The optimizer system can serve optimized ads to any site on theInternet. In addition to the above-described features, it is intendedthat the optimizer system will remain able to target ads to specificdemographics. For example, the advertiser can target ads only to usersbetween the ages of 25 and 35, or to users who live in zip codes whichrepresent upper-level incomes. Such targeting is independent of theoptimization scheme described above. In other words, an advertiser cantarget any age group it desires, regardless of whether or not that groupaligns with one of the aforementioned data bins. Relatedly, theoptimizer system can be overridden. If an advertiser wishes to purchasethe right to an entire section of the website or webpage, the ads cansimply be placed there without having to compete their way into thatposition on the page.

The foregoing description of a preferred embodiment of the invention hasbeen presented for purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formsdisclosed. Obviously, many modifications and variations will be apparentto practitioners skilled in this art. It is intended that the scope ofthe invention be defined by the following claims and their equivalents.

1. A method of displaying a plurality of advertisements on a pageviewable by a user on a computer display, comprising: storing aplurality of advertisements, each advertisements including a link toinformation that is able to be invoked by a user identifying theadvertisement by use of a computer pointing device; calculating theprobability that a user will invoke the link associated with anadvertisement displayed to the user; and displaying at least some of theadvertisements to the user, where the advertisements are arrangedrelative to one another on the page in descending order as a function ofthe calculated probability that the user will invoke the link for eachdisplayed advertisement and a price per click-through associated witheach advertisement.
 2. The method according to claim 1, wherein thedisplayed advertisements are arranged on the page in descending orderusing the product of the calculated probability and the price per clickthrough.
 3. The method according to claim 2, wherein a storedadvertisement may have associated with it at least one constraint on thedisplay of the advertisement.
 4. The method according to claim 3,wherein one constraint is a restriction on pages or positions where theadvertisement may be displayed.
 5. The method according to claim 3,wherein one constraint is a restriction on a demographic variable of auser to whom the advertisement should be displayed.
 6. The methodaccording to claim 1, wherein the calculated probability is based, atleast in part, on the prior performance of the advertisement.
 7. Themethod according to claim 6, wherein when an advertisement is new, thecalculated probability initially is set to an overestimated value sothat the calculated probability converges from above.
 8. A method ofprioritizing the placement of objects displayed to a user on a computerdisplay, comprising: storing a plurality of objects in a database, eachobject including at least one of graphics and text, having a link toinformation, and being associated with a client of a service fordisplaying objects; calculating a service revenue amount associated witheach object; ordering at least some of the objects relative to oneanother using the calculated revenue amounts for the objects; anddisplaying a plurality of the ordered objects to a user; wherein therevenue amount for each object comprises the product of a click-throughpercentage and a cost-per-click, where the click-through percentage isan estimation of the probability that a user will click on the link forthe displayed object and the cost-per-click is a monetary amount theclient will pay the service if a user clicks on the link for thedisplayed object.
 9. The method according to claim 8, wherein thedisplayed objects are arranged in decreasing visual prominence indecreasing order of calculated revenue amount.
 10. The method accordingto claim 9, wherein a stored object may have associated with it at leastone constraint on the display of the object.
 11. The method according toclaim 10, wherein the at least one constraint is at least one of arestriction on pages or positions where the object may be displayed anda restriction on a demographic variable of a user to whom the objectshould be displayed.
 12. The method according to claim 8, wherein theclick-through percentage is based, at least in part, on the priorperformance of the object.
 13. The method according to claim 12, whereinwhen an object is new, the click-through percentage initially is set toan overestimated value so that the click-through percentage convergesfrom above.
 14. The method according to claim 8, wherein the revenueamount for each object additionally includes an amount of revenue perimpression, which is a monetary amount the client will pay the servicefor each time the object is displayed.
 15. The method according to claim14, wherein the amount of revenue per impression may be zero for atleast some of the objects.
 16. A method of displaying a plurality ofgraphical objects on a webpage accessible by a user over the Internet,comprising: storing a plurality of graphical objects, each graphicalobject including a link to further information on the Internet that canbe invoked by a user, and having an associated click-through percentageand cost-per-click; and displaying at least some of the graphicalobjects on a webpage displayed to a user, wherein the graphical objectsare arranged relative to one another using the product of theclick-through percentage and the cost-per-click associated with therespective displayed graphical objects.
 17. The method according toclaim 16, wherein the displayed graphical objects are arranged indecreasing order of said product.
 18. The method according to claim 16,wherein each graphical object includes at least one of graphic and textsymbols.
 19. The method according to claim 16, wherein each graphicalobject is one of an advertisement and a topic title.
 20. The methodaccording to claim 16, wherein a stored graphical object may haveassociated with it at least one constraint on the display of thegraphical object.
 21. The method according to claim 20, wherein the atleast one constraint includes at least one of a restriction on pages orpositions where the graphical object may be displayed and a restrictionon a demographic variable of a user to whom the graphical object shouldbe displayed.
 22. The method according to claim 16, wherein theclick-through percentage is based, at least in part, on the priorperformance of the graphical object.